{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model: Random Forest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import _pickle as pickle\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import precision_score, recall_score, accuracy_score, f1_score, confusion_matrix, classification_report\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading in Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel('../top10_features.xlsx')\n",
    "df = df.drop(df.columns[0], axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scaling the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "\n",
    "features_df = df.drop([\"Decision\"], 1)\n",
    "\n",
    "scaled_df = pd.DataFrame(scaler.fit_transform(features_df), \n",
    "                               index=features_df.index, \n",
    "                               columns=features_df.columns)\n",
    "\n",
    "df = scaled_df.join(df.Decision)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop([\"Decision\"], 1)\n",
    "y = df.Decision\n",
    "\n",
    "# Train, test, split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Helper Functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Function for plotting confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_confusion_matrix(y_true, y_pred, labels=[\"Sell\", \"Buy\", \"Hold\"], \n",
    "                          normalize=False, title=None, cmap=plt.cm.coolwarm):\n",
    "\n",
    "    cm = confusion_matrix(y_true, y_pred)\n",
    "    fig, ax = plt.subplots(figsize=(12,6))\n",
    "    im = ax.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    ax.figure.colorbar(im, ax=ax)\n",
    "    # We want to show all ticks...\n",
    "    ax.set(xticks=np.arange(cm.shape[1]),\n",
    "           yticks=np.arange(cm.shape[0]),\n",
    "           # ... and label them with the respective list entries\n",
    "           xticklabels=labels, yticklabels=labels,\n",
    "           title=title,\n",
    "           ylabel='ACTUAL',\n",
    "           xlabel='PREDICTED')\n",
    "    # Rotate the tick labels and set their alignment.\n",
    "    plt.setp(ax.get_xticklabels(), rotation=45, ha=\"right\",\n",
    "             rotation_mode=\"anchor\")\n",
    "    # Loop over data dimensions and create text annotations.\n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 1.5\n",
    "    for i in range(cm.shape[0]):\n",
    "        for j in range(cm.shape[1]):\n",
    "            ax.text(j, i, format(cm[i, j], fmt),\n",
    "                    ha=\"center\", va=\"center\",\n",
    "                    color=\"snow\" if cm[i, j] > thresh else \"orange\",\n",
    "                    size=26)\n",
    "    ax.grid(False)\n",
    "    fig.tight_layout()\n",
    "    return ax"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modeling\n",
    "The preferred evaluation metric used will be __Precision__ for each class.  They will be optimized using the __F1 Score-Macro-Average__ to balance the Precision and Recall.  This is done because we want to not only be correct when predicting but also make a decent amount of predictions for each class.  Classes such as 'Buy' and 'Sell' are more important than 'Hold'."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fitting and Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "                       max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, n_estimators=10,\n",
       "                       n_jobs=None, oob_score=False, random_state=None,\n",
       "                       verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Importing the model\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# Fitting and training\n",
    "clf = RandomForestClassifier()\n",
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Printing out Evaluation Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "        Sell       0.80      0.50      0.62         8\n",
      "         Buy       0.60      0.75      0.67         8\n",
      "        Hold       0.50      1.00      0.67         1\n",
      "\n",
      "    accuracy                           0.65        17\n",
      "   macro avg       0.63      0.75      0.65        17\n",
      "weighted avg       0.69      0.65      0.64        17\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Classifier predictions\n",
    "pred = clf.predict(X_test)\n",
    "\n",
    "#Printing out results\n",
    "report = classification_report(y_test, pred, target_names=['Sell', 'Buy', 'Hold'])\n",
    "print(report)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(y_test, pred, title=\"Confusion Matrix\")\n",
    "np.set_printoptions(precision=1)\n",
    "# Plot non-normalized confusion matrix\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tuning Model Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Parameters to Tune\n",
    "params = {'n_estimators': [10,25,50,100,200],\n",
    "          'criterion': ['gini', 'entropy'],\n",
    "          'max_depth': [None, 2, 5, 10],\n",
    "          'min_samples_split': [5,10],\n",
    "          'min_samples_leaf': [1, 2, 5]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 240 candidates, totalling 720 fits\n",
      "[CV] criterion=gini, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=10 \n",
      "[CV]  criterion=gini, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=10, score=(train=0.845, test=0.255), total=   0.0s\n",
      "[CV] criterion=gini, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=10 \n",
      "[CV]  criterion=gini, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=10, score=(train=0.865, test=0.204), total=   0.0s\n",
      "[CV] criterion=gini, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=10 \n",
      "[CV]  criterion=gini, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=10, score=(train=0.928, test=0.437), total=   0.0s\n",
      "[CV] criterion=gini, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=25 \n",
      "[CV]  criterion=gini, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=25, score=(train=0.961, test=0.287), total=   0.0s\n",
      "[CV] criterion=gini, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=25 \n",
      "[CV]  criterion=gini, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=25, score=(train=0.966, test=0.321), total=   0.0s\n",
      "[CV] criterion=gini, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=25 \n",
      "[CV]  criterion=gini, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=25, score=(train=0.931, test=0.385), total=   0.0s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.0s remaining:    0.0s\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.0s remaining:    0.0s\n"
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    },
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     "output_type": "stream",
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      "\n",
      "[CV] criterion=gini, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=50 \n",
      "[CV]  criterion=gini, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=50, score=(train=0.961, test=0.238), total=   0.1s\n",
      "[CV] criterion=gini, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=50 \n"
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    },
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     "output_type": "stream",
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      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
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      "[CV]  criterion=gini, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=50, score=(train=1.000, test=0.181), total=   0.1s\n",
      "[CV] criterion=gini, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=50 \n",
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      "[CV] criterion=gini, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=100 \n",
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      "[CV] criterion=gini, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=100 \n",
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      "[CV] criterion=gini, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=100 \n",
      "[CV]  criterion=gini, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=100, score=(train=0.948, test=0.352), total=   0.3s\n",
      "[CV] criterion=gini, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=200 \n"
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    },
    {
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     "output_type": "stream",
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      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
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      "[CV]  criterion=gini, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=200, score=(train=0.961, test=0.306), total=   0.3s\n",
      "[CV] criterion=gini, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=200 \n"
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      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
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      "[CV] criterion=gini, max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=50 \n"
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      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
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      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
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      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
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      "  'precision', 'predicted', average, warn_for)\n"
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      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
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      "  'precision', 'predicted', average, warn_for)\n"
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      "  'precision', 'predicted', average, warn_for)\n",
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      "  'precision', 'predicted', average, warn_for)\n",
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      "  'precision', 'predicted', average, warn_for)\n"
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      "  'precision', 'predicted', average, warn_for)\n",
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      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
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      "  'precision', 'predicted', average, warn_for)\n"
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      "  'precision', 'predicted', average, warn_for)\n"
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      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
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      "  'precision', 'predicted', average, warn_for)\n"
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      "  'precision', 'predicted', average, warn_for)\n"
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      "  'precision', 'predicted', average, warn_for)\n",
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      "  'precision', 'predicted', average, warn_for)\n"
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      "  'precision', 'predicted', average, warn_for)\n",
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      "  'precision', 'predicted', average, warn_for)\n",
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      "  'precision', 'predicted', average, warn_for)\n",
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     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=5, min_samples_split=10, n_estimators=25, score=(train=0.833, test=0.342), total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=5, min_samples_split=10, n_estimators=50 \n",
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=5, min_samples_split=10, n_estimators=50, score=(train=0.582, test=0.212), total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=5, min_samples_split=10, n_estimators=50 \n",
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=5, min_samples_split=10, n_estimators=50, score=(train=0.802, test=0.282), total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=5, min_samples_split=10, n_estimators=50 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=5, min_samples_split=10, n_estimators=50, score=(train=0.781, test=0.339), total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=5, min_samples_split=10, n_estimators=100 \n",
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=5, min_samples_split=10, n_estimators=100, score=(train=0.566, test=0.262), total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=5, min_samples_split=10, n_estimators=100 \n",
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=5, min_samples_split=10, n_estimators=100, score=(train=0.734, test=0.469), total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=5, min_samples_split=10, n_estimators=100 \n",
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=5, min_samples_split=10, n_estimators=100, score=(train=0.860, test=0.339), total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=5, min_samples_split=10, n_estimators=200 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=5, min_samples_split=10, n_estimators=200, score=(train=0.600, test=0.306), total=   0.2s\n",
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=5, min_samples_split=10, n_estimators=200 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=5, min_samples_split=10, n_estimators=200, score=(train=0.704, test=0.268), total=   0.3s\n",
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=5, min_samples_split=10, n_estimators=200 \n",
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=5, min_samples_split=10, n_estimators=200, score=(train=0.781, test=0.339), total=   0.2s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "[Parallel(n_jobs=1)]: Done 720 out of 720 | elapsed:  1.6min finished\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:813: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=3, error_score='raise-deprecating',\n",
       "             estimator=RandomForestClassifier(bootstrap=True, class_weight=None,\n",
       "                                              criterion='gini', max_depth=None,\n",
       "                                              max_features='auto',\n",
       "                                              max_leaf_nodes=None,\n",
       "                                              min_impurity_decrease=0.0,\n",
       "                                              min_impurity_split=None,\n",
       "                                              min_samples_leaf=1,\n",
       "                                              min_samples_split=2,\n",
       "                                              min_weight_fraction_leaf=0.0,\n",
       "                                              n_estimators=10, n_jobs=None,\n",
       "                                              oob_score=False,\n",
       "                                              random_state=None, verbose=0,\n",
       "                                              warm_start=False),\n",
       "             iid='warn', n_jobs=None,\n",
       "             param_grid={'criterion': ['gini', 'entropy'],\n",
       "                         'max_depth': [None, 2, 5, 10],\n",
       "                         'min_samples_leaf': [1, 2, 5],\n",
       "                         'min_samples_split': [5, 10],\n",
       "                         'n_estimators': [10, 25, 50, 100, 200]},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
       "             scoring='f1_macro', verbose=5)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "search = GridSearchCV(clf, params, cv=3, return_train_score=True, verbose=5, scoring='f1_macro')\n",
    "\n",
    "search.fit(X,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tuned Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Training Score: 0.7628720503679642\n",
      "Mean Testing Score: 0.6992884510125891\n",
      "\n",
      "Best Parameter Found:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'criterion': 'entropy',\n",
       " 'max_depth': None,\n",
       " 'min_samples_leaf': 5,\n",
       " 'min_samples_split': 5,\n",
       " 'n_estimators': 10}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Mean Training Score:\", np.mean(search.cv_results_['mean_train_score']))\n",
    "print(\"Mean Testing Score:\", search.score(X, y))\n",
    "print(\"\\nBest Parameter Found:\")\n",
    "search.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model with the Best Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='entropy',\n",
       "                       max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=5, min_samples_split=5,\n",
       "                       min_weight_fraction_leaf=0.0, n_estimators=10,\n",
       "                       n_jobs=None, oob_score=False, random_state=None,\n",
       "                       verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "search_clf = search.best_estimator_\n",
    "\n",
    "search_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Results from Optimum Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "        Sell       0.80      0.50      0.62         8\n",
      "         Buy       0.64      0.88      0.74         8\n",
      "        Hold       1.00      1.00      1.00         1\n",
      "\n",
      "    accuracy                           0.71        17\n",
      "   macro avg       0.81      0.79      0.78        17\n",
      "weighted avg       0.73      0.71      0.70        17\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Classifier predictions\n",
    "s_pred = search_clf.predict(X_test)\n",
    "\n",
    "#Printing out results\n",
    "report = classification_report(y_test, s_pred, target_names=['Sell', 'Buy', 'Hold'])\n",
    "print(report)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confusion Matrix for Optimum Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(y_test, s_pred, title=\"Confusion Matrix\")\n",
    "np.set_printoptions(precision=1)\n",
    "# Plot non-normalized confusion matrix\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
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 "nbformat": 4,
 "nbformat_minor": 2
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